torchsig.datasets.datasets.TorchSigIterableDataset¶

class torchsig.datasets.datasets.TorchSigIterableDataset(signal_generators: str | ConcatSignalGenerator | list = 'all', transforms: list[Transform | callable] = [], component_transforms: list[Transform | callable] = [], target_labels: list | None = None, validate_init: bool = True, **kwargs)[source]¶

Bases: HierarchicalMetadataObject, IterableDataset

Base class for generating signals.

The dataset will continue to generate samples infinitely.

signal_generators¶: The signal generators to use. Can be a string, ConcatSignalGenerator, or list.

transforms¶: List of transforms to apply to the entire signal.

component_transforms¶: List of transforms to apply to individual signal components.

target_labels¶: Labels to extract from the signal.

validate_init¶: Whether to validate metadata during initialization.

Methods

`add_parent`	Add parent Seedable object and set up RNGs accordingly.
`add_signal_generator`	Adds a signal generator to this dataset.
`copy`	Create a copy of the object.
`get_distribution`	Create distribution function with proper seeding.
`get_full_metadata`	Function for modifying and returning a new metadata with all the fields in parent or child, with child overriding parent in conflicts.
`get_second_seed`	Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds.
`init_signal_generator`	Initializes the signal generator.
`key_lookup`	Lookup a metadata key with enhanced error reporting.
`keys`	Get all metadata keys.
`seed`	Seed number generators with given seed.
`setup_rngs`	Initialize torch and numpy number generators, and update its children.
`update_from_parent`	Update numpy and torch number generators with parent seed.
`validate_metadata_fields`	Validates signal metadata for each signal generators.

__init__(signal_generators: str | ConcatSignalGenerator | list = 'all', transforms: list[Transform | callable] = [], component_transforms: list[Transform | callable] = [], target_labels: list | None = None, validate_init: bool = True, **kwargs)[source]¶

Initializes the dataset.

Parameters:

signal_generators – The signal generators to use. Can be a string, ConcatSignalGenerator, or list.
transforms – List of transforms to apply to the entire signal.
component_transforms – List of transforms to apply to individual signal components.
target_labels – Labels to extract from the signal.
validate_init – Whether to validate metadata during initialization.
**kwargs – Additional keyword arguments passed to the parent class.

init_signal_generator(signal_generator: str | callable) → None[source]¶

Initializes the signal generator.

Parameters:: signal_generator – The signal generator to be initialized. If a string, it is first looked up to retrieve the corresponding signal generator function.
Raises:: TypeError – If the signal_generator is neither a string nor a callable.

add_signal_generator(signal_generator: callable, class_name: str | None = None, class_index: int | None = None, likelihood: int = 1) → None[source]¶

Adds a signal generator to this dataset.

Parameters:

signal_generator – A callable object which takes no arguments and returns a Signal.
class_name – (optional) A name for this signal class in the dataset. If None, the signal will be generated and added to the data, but no labels will be made for the signal.
likelihood – (optional) The relative likelihood of this signal type in the dataset. Doubling the likelihood will make this signal twice as likely to be placed in the data.

validate_metadata_fields() → bool[source]¶

Validates signal metadata for each signal generators.

Returns:: Whether Signal metadata is valid.

__call__() → Signal | ndarray | tuple[source]¶

Same as next(); returns the next item in the dataset.

Allows datasets to be treated as signal generators for other datasets.

__repr__() → str[source]¶

Returns a string representation of the dataset.

Returns:: String representation of the dataset.

add_parent(parent: Seedable, register: bool = True) → None¶

Add parent Seedable object and set up RNGs accordingly.

Parameters:

parent – Parent Seedable object to add.
register – If True (default), add self to parent.children so that future seed propagation reaches this object. Pass False for transient objects (e.g. per-sample Signal instances) that only need the parent link for metadata/RNG access during their lifetime but must not accumulate in the parent’s child list, which would otherwise cause unbounded memory growth.

copy() → HierarchicalMetadataObject¶

Create a copy of the object.

Returns:: A new instance of the same class with the same metadata and parent.

Example

>>> obj = HierarchicalMetadataObject(metadata={"key": "value"})
>>> copy_obj = obj.copy()
>>> copy_obj["key"]
'value'

get_distribution(params: list | tuple | float, scaling: str = 'linear') → Distribution¶

Create distribution function with proper seeding.

Parameters:

params – Parameters for distribution.
scaling – Scaling param for distribution. Defaults to ‘linear’.

Returns:

Distribution function, seeded.

Return type:

Distribution

get_full_metadata() → dict[str, Any]¶

Function for modifying and returning a new metadata with all the fields in parent or child, with child overriding parent in conflicts.

Returns:: Dictionary containing all metadata from parent and child, with child values overriding parent values in case of conflicts.

Example

>>> parent = HierarchicalMetadataObject(metadata={"field_1": 4, "field_2": 5})
>>> child = HierarchicalMetadataObject(parent=parent, metadata={"field_2": 6})
>>> child.get_full_metadata()
{'field_1': 4, 'field_2': 6}

get_second_seed(seed: int) → int¶

Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds.

Parameters:: seed – Seed to use.
Returns:: New seed.

key_lookup(key: str) → Any¶

Lookup a metadata key with enhanced error reporting.

Parameters:: key – The metadata key to lookup.
Returns:: The value associated with the key.
Raises:: MetadataAttributeError – If the key is not found in the metadata or parent metadata.

Example

>>> obj = HierarchicalMetadataObject(metadata={"key": "value"})
>>> obj.key_lookup("key")
'value'

keys() → list[str]¶

Get all metadata keys.

Returns:: List of all metadata keys.

Example

>>> obj = HierarchicalMetadataObject(metadata={"key1": 1, "key2": 2})
>>> list(obj.keys())
['key1', 'key2']

seed(seed: int) → None¶

Seed number generators with given seed.

Parameters:: seed – Seed to use.

setup_rngs() → None¶: Initialize torch and numpy number generators, and update its children.

update_from_parent() → None¶: Update numpy and torch number generators with parent seed.